Knowledge Representation

Formalisms and ontologies

Natural language (such as English) is inherently ambiguous and can contain contradictions and omit details, yet it is commonly used to disseminate methods, experiments, and results in scientific papers. Moreover, natural language represents a barrier for applying automated computational methods to the vast amount of scientific information available in the literature. We investigate formalisms and ontologies based on mathematical language to unambiguously describe complex experimental procedures and their morphological outcomes. Using mathematical descriptions is not only necessary for semantic clarity for human scientists, but a requirement for the application of artificial intelligence systems able to understand the data.

Databases

Based on mathematical formalisms and ontologies, we develop and curate databases that centralize the experimental knowledge disseminated in the literature. These databases represent an extraordinary resource for the community, paving the way for the streamlining of scientific data. In addition, the mathematical nature of the formalisms and ontologies that these databases are built with allows the direct application of artificial intelligence systems to access and understand the data. Indeed, our curated databases represent the first step towards the application of automated systems that can extract new knowledge and reverse-engineer mechanistic models from the outstanding results obtained at the bench.

Expert systems

In addition to the raw information stored in the databases, we develop user-friendly graphical interfaces that permit any scientist to access and mine these resources. We create applications for the visualization and optimized search of specific data. For example, all morphological outcomes reported in the literature resulting from certain pharmacological or genetic intervention can be listed and visualized in the application in seconds. Furthermore, these applications can be used for easily formalizing new experiments, which can then be shared and included in scientific publications.